Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Cholinergic regulation of bone.

Bone remodeling is regulated by the two branches of the autonomic nervous system: the adrenergic and the cholinergic branches. Adrenergic activity favors bone loss, whereas cholinergic activity has been recently shown to favor bone mass accrual. In vitro studies have reported that cholinergic activity induces proliferation and differentiation of bone cells. In vivo studies have shown that the inhibition of cholinergic activity favors bone loss, whereas its stimulation favors bone mass accrual. Clinical studies have shown that bone density is associated with the function of many cholinergic-regulated tissues such as the hypothalamus, salivary glands, lacrimal glands and langerhans cells, suggesting a common mechanism of control. Altogether, these observations and linked findings are of great significance since they improve our understanding of bone physiology. These discoveries have been successfully used recently to investigate new promising therapies for bone diseases based on cholinergic stimulation. Here, we review the current understanding of the cholinergic activity and its association with bone health.